Urban aerosol sources are important due to the health effects of particles
and their potential impact on climate. Our aim has been to quantify and
parameterise the urban aerosol source number flux <i>F</i> (particles
m<sup>&minus;2</sup> s<sup>&minus;1</sup>), in order to help improve how this source is represented
in air quality and climate models. We applied an aerosol eddy covariance
flux system 118.0 m above the city of Stockholm. This allowed us to measure
the aerosol number flux for particles with diameters &gt;11 nm. Upward source
fluxes dominated completely over deposition fluxes in the collected dataset.
Therefore, the measured fluxes were regarded as a good approximation of the
aerosol surface sources. Upward fluxes were parameterised using a traffic
activity (<I>TA</I>) database, which is based on traffic intensity measurements.
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The footprint (area on the surface from which sources and sinks affect flux
measurements, located at one point in space) of the eddy system covered road
and building construction areas, forests and residential areas, as well as
roads with high traffic density and smaller streets. We found pronounced
diurnal cycles in the particle flux data, which were well correlated with
the diurnal cycles in traffic activities, strongly supporting the conclusion
that the major part of the aerosol fluxes was due to traffic emissions.
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The emission factor for the fleet mix in the measurement area
<I>EF</I><sub><i>fm</i></sub>=1.4&plusmn;0.1&times;10<sup>14</sup> veh<sup>&minus;1</sup> km<sup>&minus;1</sup> was deduced. This agrees
fairly well with other studies, although this study has an advantage of
representing the actual effective emission from a mixed vehicle fleet.
Emission from other sources, not traffic related, account for a
<I>F</I><sub>0</sub>=15&plusmn;18&times;10<sup>6</sup> m<sup>&minus;2</sup> s<sup>&minus;1</sup>. The urban aerosol source flux
can then be written as <I>F=EF</I><sub><i>fm</i></sub><I>TA+F</I><sub>0</sub>. In a second attempt to find a
parameterisation, the friction velocity <i>U</i><sub>*</sub> normalised with the
average friction velocity <!-- MATH
$\overline{U_\ast}$
-->
<IMG WIDTH="21" HEIGHT="36" ALIGN="MIDDLE" BORDER="0"
src="acp-6-769-img15.gif"
ALT="$overline{U_ast}$"> has been included,
<I>F=EF</I><!-- MATH
$_{fm }TA\left({\frac{U_\ast }{\overline{U_\ast}}}\right)^{0.4}{+}F_{0}$
-->
<IMG WIDTH="136" HEIGHT="51" ALIGN="MIDDLE" BORDER="0"
src="acp-6-769-img16.gif"
ALT="$_{fm }TAleft({frac{U_ast }{overline{U_ast}}}right)^{0.4}{+}F_{0}$">.
This parameterisation results in a somewhat reduced emission
factor, 1.3&times;10<sup>14</sup> veh<sup>&minus;1</sup> km<sup>&minus;1</sup>. When multiple linear regression
have been used, two emission factors are found, one for light duty vehicles
<I>EF</I><sub>LDV</sub>=0.3&plusmn;0.3&times;10<sup>14</sup> veh<sup>&minus;1</sup> km<sup>&minus;1</sup> and one for heavy-duty
vehicles, <I>EF</I><sub>HDV</sub>=19.8&plusmn;4.0&times;10<sup>14</sup> veh<sup>&minus;1</sup> km<sup>&minus;1</sup>, and
<i>F</I><sub>0</sub>=19&plusmn;16&times;10<sup>6</sup> m<sup>&minus;2</sup> s<sup>&minus;1</sup>. The results show that during
weekdays ~70&ndash;80% of the emissions came from HDV.